Building Graph RAG Systems with LangChain and Neo4j

Traditional retrieval-augmented generation often struggles to capture complex relationships across structured data. By integrating graph databases with large language models, you can query interconnected knowledge with precision and context. In this written guide, you will understand how to represent relational domains as knowledge graphs and interface them with language models. You will explore practical patterns for translating natural language into database queries, combining vector search with graph traversal, and optimizing answers for clarity and accuracy. What you'll learn: - Understand core concepts of knowledge graphs, Cypher query syntax, and graph schemas in Neo4j - Configure LangChain pipelines to interact directly with graph structures - Construct Graph RAG workflows that convert user prompts into accurate graph queries - Apply hybrid retrieval techniques combining semantic vector search with graph relationships - Implement validation strategies and prompt templates to reduce hallucinations - Structure structured entity extraction to populate and query graph databases systematically The course begins with foundational terminology and database concepts before progressing step-by-step through pipeline architecture, query translation, and hybrid retrieval patterns. This material is designed for developers, data practitioners, and AI enthusiasts seeking a practical introduction to graph-powered language applications without prior graph database experience. Start reading today to expand your skills in modern knowledge retrieval and graph-assisted language models.